ed and combined with evaluations of fracture characteristics, including two-dimensional (2D) and 3D analyses, to comprehensively describe both the morphologies and injury patterns of TPFIPs.
In this study, a method involving a simulated injury pattern was developed and combined with evaluations of fracture characteristics, including two-dimensional (2D) and 3D analyses, to comprehensively describe both the morphologies and injury patterns of TPFIPs.We conducted this study to investigate the prevalence of potential chemo-response-related gene mutations in triple-negative breast cancer (TNBC) patients and to evaluate the potential relationship between these gene mutations and neoadjuvant chemotherapy response in TNBC patients.
One hundred sixty-two TNBC patients in Fudan University Shanghai Cancer Center who received NAC with 4 cycles of paclitaxel and carboplatin were enrolled in this study. Fifty-six pathological complete response (pCR) patients and 56 non-pCR patients were enrolled in this retrospective study for the training set. Clinical assessments of postoperative residual tumors were performed according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria. Forty chemo-response-related genes were screened in each tumor specimen by second-generation sequencing analysis. Fifty TNBC patients who received neoadjuvant chemotherapy with paclitaxel and carboplatin were enrolled in the validation group.
Fifty-seven of 112 (50.9%) TNBadjuvant chemotherapy based on paclitaxel and carboplatin. Our findings need to be validated through follow-up studies in this and additional cohorts.
A subset of TNBC patients carry deleterious somatic mutations in 10 HR-related genes. The mutation status of this expanded gene panel is likely to effectively predict respond rate to neoadjuvant chemotherapy based on paclitaxel and carboplatin. Our findings need to be validated through follow-up studies in this and additional cohorts.Axillary lymph node (ALN) staging is essential in predicting the clinical outcome of breast cancer (BC) patients. Traditionally, it follows the tumor-node-metastasis (TNM) staging, but its accuracy needs further improvement.
A total of 9,616 BC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 675 patients from the First Affiliated Hospital of China Medical University underwent mastectomy together with ALN dissection were reviewed. Univariate and multivariate logistic analyses were conducted to find the most meaningful factors relevant to prognosis.
After univariate and multivariate analyses, age, race, primary site, radiation, chemotherapy, grade, T-stage, estrogen receptor (ER), progesterone receptor (PR), total number of positive lymph nodes (pN), positive lymph node ratio (LNR) and log odds of positive LNs (LODDS) were found to be significantly associated with overall survival (OS). Using these non-LN risk factors, we further compared the efficacy of three different A aid in guiding postoperative treatment.
Compared with pN and LNR, LODDS showed higher homeostasis in LN evaluation, and showed marked efficacy in evaluating survival differences among patients with negative LN staging. We constructed a BC prognosis model by incorporating highly relevant clinical pathological factors and a new method of LN staging, which may greatly aid in guiding postoperative treatment.The role of thoracic consolidation radiotherapy in patients with extensive stage small cell lung cancer (ES-SCLC) remains controversial. This study aimed to evaluate the efficacy of thoracic radiotherapy (TRT) in these patients.
A systematic literature search was performed in PubMed, Embase, and the Cochrane library to identify qualified clinical studies. The hazard ratios (HRs) and 95% confidence intervals (CIs) of overall survival (OS), progression-free survival (PFS) and local recurrence-free survival (LRFS) were extracted, and toxicity of the TRT group versus non-TRT group was analyzed.
A total of 12 studies were included in this meta-analysis, including 936 patients in the TRT group and 1,059 patients in the non-TRT group. The combined results showed that TRT significantly improved OS (HR =0.65; 95% CI 0.55-0.77, P&lt;0.00001), PFS (HR =0.64; 95% CI 0.56-0.72, P&lt;0.00001) and LRFS (HR =0.38, 95% CI 0.26-0.53, P&lt;0.00001). Subgroup analysis showed that OS benefits were observed in patients receiving sequential TRT (HR =0.67; 95% CI 0.54-0.84, P=0.0006). The addition of TRT significantly improved OS in patients over 65 years of age (HR =0.55; 95% CI 0.40-0.74, P=0.0001). For patients with only one organ metastasis, there was no significant difference in OS between the two groups (HR =0.61; 95% CI 0.36-1.01, P=0.06). There was no statistical difference in hematologic toxicity (leukopenia, thrombocytopenia, anemia) and non-hematologic toxicity (nausea or vomiting) between the two groups. The incidence of grade ?3 esophageal toxicity was 4.6% in the TRT group and 0% in the non-TRT group (P=0.0001). Grade ?3 bronchopulmonary toxicity was 2.9% in the TRT group and 0.8% in the non-TRT group (P=0.02).
TRT improves OS, PFS and LRFS in patients with ES-SCLC, with a low increase in esophageal and bronchopulmonary toxicity. More randomized controlled trials (RCTs) are expected to confirm our conclusions.
CRD42020190575.
CRD42020190575.To investigate the feasibility of integrating global radiomics and local deep features based on multi-modal magnetic resonance imaging (MRI) for developing a noninvasive glioma grading model.
In this study, 567 patients [211 patients with glioblastomas (GBMs) and 356 patients with low-grade gliomas (LGGs)] between May 2006 and September 2018, were enrolled and divided into training (n=186), validation (n=47), and testing cohorts (n=334), respectively. All patients underwent postcontrast enhanced T1-weighted and T2 fluid-attenuated inversion recovery MRI scanning. Radiomics and deep features (trained by 8,510 3D patches) were extracted to quantify the global and local information of gliomas, respectively. A kernel fusion-based support vector machine (SVM) classifier was used to integrate these multi-modal features for grading gliomas. https://www.selleckchem.com/products/bay-11-7085.html The performance of the grading model was assessed using the area under receiver operating curve (AUC), sensitivity, specificity, Delong test, and -test.
The AUC, sensitivity, and specificity of the model based on combination of radiomics and deep features were 0.